Abstract: A system and method are provided for eliciting interesting structure from a collection of entities or resources with explicit and/or implicit, static and/or dynamic relations, called “affinities,” between them. Interesting structure includes (1) notions of quality, authority, or definitiveness of information, (2) notions of relevance to a user's information need, (3) notions of similarity among the plurality of resources retrieved from a universe of resources by a query process, and (4) notions of similarity among the usages of resources by different users/servers. Similarities between entities are computed, based on similarities between the affinity values for the entities. That is, where the affinitiy values for two entities resemble each other, the two entities have a high degree of similarity. Using the similarities, the entities are ranked, clustered, etc., based on a significance derived from the similarities. The ranking, clustering, etc., makes up the interesting structure which is sought.

Abstract: A system and method are provided for searching for desired items from a network of information resources. In particular, the system and method have advantageous applicability to searching for World Wide Web pages having desired content. An initial set of pages are selected, preferably by running a conventional keyword-based query, and then further selecting pages pointing to, or pointed to from, the pages found by the keyword-based query. Alternatively, the invention may be applied to a single page, where the initial set includes pages pointed to by the single page and pages which point to the single page. Then, iteratively, authoritativeness values are computed for the pages of the initial set, based on the number of links to and from the pages. One or more communities, or "neighborhoods", of related pages are defined based on the authoritativeness values thus produced.

Abstract: A system and method are provided for performing the process known as "data mining" on a database of raw data records having common data elements, to obtain categorical cluster rules as to what elements of the data tend to occur in common in multiple records. Initial values are assigned to the elements. In an iterative process, the associated value for each given one of the elements is recalculated based on the values of other elements which occur in records together with the given element. Thus, the associated values will tend to grow for elements occurring together in multiple records. Those common occurrences of elements in multiple records represent categorical cluster rules the owner of the data is likely to want to know about. Thus, these rules may be identified based on the growth of the associated values for the records.